Cumulative image recognition method and application program for the same

ABSTRACT

A cumulative image recognition method performs a matching analysis on frame and matching data in a mobile device when the mobile device focuses on a recognition target and captures frame of the image of the recognition target. The method obtains the feature value of each matching data according to the matching result, and determines if one of the matching data has an optimal result matching the recognition target according to several entries of feature values. If no optimal result, then the method respectively sums up the feature value of each matching data into a summing feature value and determines if the plurality of the matching data has a candidate result similar to the recognition target according to several entries of the summing feature values. If no candidate result, the mobile device captures the next frame on the recognition target, and performs next matching analysis on the next frame.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to an image recognition method, inparticular further relates to an image recognition method for providingan optimal result or a candidate result of recognition to users.

2. Description of Related Art

For the convenience of users to check related data of a physicalproduct, technologies of the image recognition become popular in recentyears. The image recognition takes a photo on a physical product, anddetermines what the physical product is after analyzing and recognizingthe photo, and retrieves the related data of the physical productoffering to users for reference.

Generally speaking, the current image recognition technologies arecategorized into online recognition and offline recognition. The offlinerecognition integrates the required data by the recognition in anoffline device, such as a personal computer or a mobile device, and theoffline device executes the recognition operation. The onlinerecognition transfers the photo of the physical product captured byusers to an online server and the server performs the recognitionoperation.

When using an offline device for performing offline recognition, usersare allowed to obtaining recognition result the fastest because theoperation is not involved with network data transfer. Nonetheless,because the offline device has limited saving space, the offeredmatching data quantity is limited as a result. Though the offlinerecognition provides faster recognition, but the accuracy rates are low.

Alternatively, when using a server for performing online recognition,the required recognition time is much longer than the offlinerecognition because the operations involves with the network datatransfer, for example uploading photos taken by users, and transferringrecognition results to the devices of the users via networks after theserver completes recognition. Nonetheless, the saving space of serversare large, and are capable of saving large amount of the matching dataand the accuracy rates accordingly are much higher than the offlinerecognition.

As a result, it is the focus of the inventor to develop a technologywhich provides the advantages of offline recognition and onlinerecognition.

SUMMARY OF THE INVENTION

The objective of the present invention is to provide a cumulative imagerecognition method and application for implementing the method, which isused when an optimal result is not generated by recognizing one frame,several frames are captured for performing multiple recognition severaltimes. The feature values generated from several recognition are summedup to obtain a candidate result according to the summing feature value,which avoids the issue occurs when the recognition of single one framewith matching data fails, the user cannot obtain the recognition result.

In order to obtain the above objective, a mobile device of the presentinvention focuses on a recognition target, the mobile device obtainsframe of the image of a recognition target, and performs a matchinganalysis between the frame and a plurality of the matching data in themobile device. Next, obtain the feature value of each matching dataaccording to the matching result and determine if the plurality of thematching data has an optimal result matching the recognition targetaccording to several feature values. If no optimal result, sum up thefeature value of each matching data respectively into a summing featurevalue, and determine if the plurality of the matching data has acandidate result similar with the recognition target according toseveral summing feature values. If no candidate result, the mobiledevice captures next frame of the recognition target, and perform nextmatching analysis on next frame.

A conventional image recognition method performs a matching analysis onsingle one photo or frame of the recognition target with a plurality ofthe matching data for obtaining the similar features between the frameand the matching data. Then determine if the recognition target matchesthe matching data according to the quantity of the similar features.Compare with the prior arts, the present invention provide an advantageis that after perform a matching analysis between a frame and aplurality of the matching data, respectively sum up the feature value ofthe plurality of the matching data if fail to recognize an optimalresult. After recognizing a plurality of frames, if the summing featurevalue of any matching data is higher than the threshold value, considerthe matching data as a candidate result similar to the recognitiontarget.

The recognition method of the present invention is useful in avoidingthe issue occur when perform a one time recognition on single one frameof the recognition target, the recognition on the frames of the sameobject taken from different capture angles and lighting may fail orsucceed. Further, if the matching data is highly similar but differentfrom the recognition target, the matching data is considered as acandidate result according to the present invention for user's referencesuch that users can obtain a recognition result.

BRIEF DESCRIPTION OF DRAWING

The features of the invention believed to be novel are set forth withparticularity in the appended claims. The invention itself, however, maybe best understood by reference to the following detailed description ofthe invention, which describes an exemplary embodiment of the invention,taken in conjunction with the accompanying drawings, in which:

FIG. 1 is an architecture schematic diagram of the first preferredembodiment according to the present invention;

FIG. 2 is a frame capturing schematic diagram of the first preferredembodiment according to the present invention;

FIG. 3 is a block diagram of a mobile device of the first preferredembodiment according to the present invention;

FIG. 4 is a recognition flowchart of the first preferred embodimentaccording to the present invention;

FIG. 5 is an optimal result recognition flowchart of the first preferredembodiment according to the present invention; and

FIG. 6 is a candidate result recognition flowchart of the firstpreferred embodiment according to the present invention.

DETAILED DESCRIPTION OF THE INVENTION

In cooperation with attached drawings, the technical contents anddetailed description of the present invention are described thereinafteraccording to a preferable embodiment, being not used to limit itsexecuting scope. Any equivalent variation and modification madeaccording to appended claims is all covered by the claims claimed by thepresent invention.

FIG. 1 and FIG. 2 are an architecture schematic diagram and a framecapturing schematic diagram of the first preferred embodiment accordingto the present invention. The present invention discloses a cumulativeimage recognition method (referred as the recognition method in thefollowing). The recognition method mainly uses in a mobile device 2carried by a user. The mobile device 2 can be a smart phone, a tablet ora notebook computer etc. In FIG. 2, a mobile phone is used as an examplebut is not limited thereto. Via the mobile device 2, the user performsrecognition on a physical recognition target 1. Upon the recognitionsucceeds, the mobile device 2 obtains a recognition result about therecognition target 1. The recognition result comprises texts, picturesand film data of the recognition target 1, offered for user's referenceabout the recognition target 1. Thus, the user can proceed to actionssuch as purchase, selling, renting the recognition target 1.

In order to implement the recognition method, a user has to operate themobile device 2 for obtaining the frame of the image 3 of therecognition target 1 via an image capturing unit 22 on the mobile device2. The image capturing unit 22 obtains the frame 3 under a cameracapturing model. Also, it should be noted that the content of the frame3 has to include the recognition target 1 in order to performrecognition on the recognition target 1.

The mobile device 2 performs the image recognition operation on theframe 3 via the application installed in the mobile device 2 (as theapplication 241 shown in FIG. 3). Lastly, the recognition result isdisplayed on the display monitor 21 for user's searching reference.Thus, the user is allowed to obtain about related information of therecognition target 1 from the recognition results, such as pictures,names, places of origin, publishers, distributors, prices and purchasemethods etc. The application 241 has executable programming codeswritten in and applicable to the mobile device 2, and after the mobiledevice 2 is installed with the application 241 and the application 241is executed, the recognition method of the present invention is executedvia the programming codes of the application 241.

FIG. 3 is a block diagram of a mobile device of the first preferredembodiment according to the present invention. As shown in FIG. 3, themobile device 2 comprises the display monitor 1, the image capturingunit 22, a microprocessor unit 23, a storage unit 24, an input unit 25and an internet unit 26. The microprocessor unit 23 is electricallycoupled to the display monitor 1, the image capturing unit 22, thestorage unit 24, the input unit 25 and the internet unit 26 forcontrolling the units 21, 22, 24, 25, 26, and transferring data andinstructions among the units 21, 22, 24, 25, 26.

The mobile device 2 obtains the frame of the image 3 of the recognitiontarget 1 via the image capturing unit 22, and performs the imagerecognition operation on the frame 3. Also, after the recognitionsucceeds, an optimal result or a candidate result obtained form therecognition is displayed on the display monitor 21 for user's searchingreference.

The application 241 is saved in the storage unit 24. The mobile device 1controls the image capturing unit 22 to obtain the frame 3, and performsimage recognition on the frame 3 via executing the application 241.Additionally, a plurality of the matching data 242 is also saved in thestorage unit 24. When the application 241 performs the imagerecognition, the application 241 performs a matching analysis betweenthe frame 3 and the plurality of the matching data 242 for determiningif the plurality of the matching data 242 has the matching data 242matching the recognition target 1. Further in details, the application241 performs an image analysis on the frame 3 and the matching data 242,and respectively acquiring one or several featuring points each similarbetween the matching data 242 and the frame 3. Thus, the application 241determines if each matching data matches the recognition target 1 in theframe 3 according to similar feature quantity (i.e. the feature value)of each matching data 242.

The input unit 25 is for example a button, a track ball, a mouse or abody sensing module offered to the user for operating the mobile device1. Additionally, the input unit 25 is integrated with the displaymonitor 21, i.e. the display monitor 21 is a display monitor providingtouch control, but is not limited thereto.

The internet unit 26 is used for facilitating the mobile device 1 toconnect to networks, for example wide area network or area network etc.Thus, the user obtains recognition result of the recognition target 1 onthe mobile device 1, and connects to the Internet via the internet unit26, for executing following operations such as purchasing, selling,renting the recognition target 1, or further checking the detailed dataof the recognition target 1 according to the recognition result.

FIG. 4 is a recognition flowchart of the first preferred embodimentaccording to the present invention. First, the mobile device 2 has tocapture a frame 3 of the recognition target 1 via the image capturingunit 22. In details, the mobile device 1 has to be operated by the userto initiate the application 241 (step S10). Thus, the application 241controls the image capturing unit 22 to enter into a camera capturingmodel. Next, the image capturing unit 22 continues to scan the externalimage (the image may include the recognition target 1), and the imagegenerated from the analysis of the application 241 for determining ifthe mobile device 2 moves (step S12). It should be noted that themovement mentioned here refers to instantaneous movement or longdistance movement instead of swing movement.

In step S12, if it is determined the mobile device 2 moves, the imagecapturing unit 22 does not capture the frame 3. Further, if the movementamount of the mobile device 2 exceeds predetermined threshold value, themobile device 2 may selectively clear the content of an similar list 244in the storage unit 24 (step S14), the related technical characteristicsof the similar list 244 are descried in the following.

In the step S12, if it is determined the mobile device 2 does not move,i.e. in a stationary status, it means that the user focuses on aspecific recognition target 1. The mobile device 2 captures a frame 3via the image capturing unit 22 (step S16). The content of the frame 3has to include the recognition target 1. Otherwise, the mobile device 2performs recognition on the recognition target 1 according to the frame3. It should be noted that when the application 241 determines themobile device 2 is in stationary, the application 241 automaticallycontrols the image capturing unit 22 to capture the frame 3, the userdoes not need to press the button manually (not shown in the diagram),the captured frame 3 is different from a normal photo.

After step S16, the application 242 captures the frame 3, and performsimage recognition operation on the recognition target 1 according to theframe 3 (step S18). As mentioned above, the content of the frame 3 hasto include the recognition target 1 (for example the book shown in FIG.2). If the content of the frame 3 include vague objects such as sky,floor or walls etc., the recognition of the application 241 fails.

After step S18, the application 241 determines if obtain one or severalrecognition results (step S20). If any recognition result is obtained,the recognition succeeds, the application 241 displays the recognitionresult on the display monitor 21 of the mobile device 2 (step S22). Onthe other hand, if no recognition result is obtained, the recognitionfails. The operation of the application 241 returns to step S12 tore-determine if the mobile device 2 moves, and captures the second framewhen the mobile device 2 is in stationary, and performs the secondrecognition according to the second frame, and so on.

FIG. 5 and FIG. 6 are respectively recognition flowcharts of an optimalresult and a candidate result of the first preferred embodimentaccording to the present invention. The steps in FIG. 5 and FIG. 6 areexecuted by the application 241. First, the application 241 captures theframe 3 of the recognition target 1 via the image capturing unit 22(step S30). Next, perform a matching analysis respectively on the frame3 and the plurality of the matching data 242 (step S32). Further, obtainfeature value from matching each matching data 242 and the frame 3according to the analysis result (step S34).

In step S32 and step S34, the application 241 performs image analysis onthe frame 3 and each matching data 242 for respectively acquires one orseveral featuring points identical or similar between each matching data242 and the frame 3, and determines if each matching data 242 match theframe 3 (i.e. the recognition target 1) according to the quantity of theidentical or similar features points. The feature value means thequantity of the similar feature points between each matching data 242and the frame 3. In other words, the matching data 242 having higherfeature value is more similar to the frame 3 (i.e. the recognitiontarget 1).

In the recognition method, the application 241 determines if a featurevalue of the matching data 242 higher than a first threshold value inthe plurality of the matching data 242. If the feature value of thematching data 242 is higher than the first threshold value, therecognition succeeds.

In other words, the application 241 considers the first threshold valueas the condition for matching the frame 3. When the feature value of thematching data 242 is higher than the first threshold value, the matchingdata 242 is considered a recognition result matching with the frame 3(i.e. the recognition target 1). Consequently, the one or severalmatching data 242 are considered an optimal result, and displayed on thedisplay monitor 21 of the mobile device 2.

In details, after step S34, the application 241 acquires the featurevalues of all matching data 242, and selectively generates a result list243 according to the feature values (step S36). In the embodiment, inorder to invalid recognitions performed by the user (i.e. the content ofthe frame 3 does not include any recognition target), the application241 determines if the feature values of the result list 243 are allzeros or all below a third threshold value (step S38). The thirdthreshold value is less than the first threshold value, the firstthreshold value for example is “30”, and the third threshold value forexample is “3”.

If the feature values of the result list 243 are all zeros or all belowa third threshold value, the application 241 clears the content in thesimilar list 244 (details in the following) (step S40). Further, theapplication 241 selectively deletes the result list (step S42).Nonetheless, in another embodiment, the application 241 re-execute theabove step S30 to step S38, and clears the content of the similar list244 and deletes the result list 243 when determines that the featurevalues of the result list 243 are all zeros or all below a thirdthreshold value for the second time, the third time or the nth time.Nonetheless, the above mentioned is a preferred embodiment of thepresent invention, the application 241 may also selectively notdetermine if the feature values of the result list 243 are all zeros orless than the third threshold values, and are not limited thereto.

In step S38, if the feature value are not all zeros, or not all lessthan the third threshold value in the result list 243, the application241 further determines if a feature value of the matching data is higherthan the first threshold value in the plurality of the matching data242, (step S44). If the feature value of the matching data is higherthan the first threshold value, the matching data 242 is considered asan optimal result matching the recognition target 1 (step S46). Theoptimal result is displayed in the display monitor 21 (step S48).Lastly, the application 241 clears the content in the similar list 244(step S50).

For example, the content of the result list 243 is as shown below:

Matching Data Feature Value First Threshold Value A 2 30 B 20 30 C 35 30

Result List

As the example shown in the list above, provided the feature value of amatching data A is “2” (i.e. the frame 3 has 2 similar feature point),the feature value of a matching data B is “20” (i.e. the frame 3 has 20similar feature points), the feature value of a matching data C is “35”(i.e. the frame 3 has 35 similar feature points), and the firstthreshold value is sets as “30”. Under the circumstance, the matchingdata C is considered an optimal result matching the recognition target1, and the matching data C is displayed on the display monitor 21.

As shown in FIG. 6, in step S44, if the application 241 determines thatno the feature value of the matching data 242 is higher than the firstthreshold value, the recognition of the frame 3 fails to generate anoptimal result. Consequently, according to the present invention, theapplication 241 continues to sum up the feature values of each matchingdata 242, and in the end obtains a candidate result via the recognitionfor the second, the third and the nth time on the second, the third andthe nth frame.

As shown in FIG. 6, the application 241 screens the matching data 242,and discards the matching data 242 having the feature values less thanthe third threshold value (step S60), i.e. sums up the feature values ofthe matching data 242 which is highly dissimilar with the recognitiontarget 1. For example, if the third threshold value is set as “5”, inthe above embodiment, the feature value of the matching data A is notsummed up.

Next, the application 241 reserves the matching data 242 having thefeature values higher than the third threshold value, and writes thefeature values of the matching data 242 in the similar list 244 forsumming up a summing feature value of each matching data 242 written inthe similar list 244 (step S62). Further in details, the application 241only sums up the feature values higher than the third threshold value tothe corresponding columns in the similar list 244, the feature valuesless than the third threshold value are not summed up.

Next, the application 241 determined if the summing feature values ofthe matching data are higher than the second threshold value accordingto the content of the similar list 244 (step S64). If the summingfeature value of the matching data is higher than the second thresholdvalue, the operation flow of the application 241 returns to the step S30for capturing next frame 3, and performs the next image recognitionoperation on the next frame 3. If the recognition on the next frame 3generates an optimal result, the optimal result is provided for user'ssearching reference. On the other hand, if the recognition on the nextframe 3 still fails to generate an optimal result, the application 241respectively sums up the feature values of each matching data 242 andthe feature value generated from matching with the next frame 3, andupdates the summing feature values of the similar list 244. In theembodiment, the second threshold value is higher than the firstthreshold value and the third threshold value, but is not limitedthereto. Lastly, when the application 241 determines a summing featurevalue of the matching data 242 is higher than the second thresholdvalue, and considers the matching data 242 as a candidate result (stepS66), and displays the candidate result on the display monitor 21 (stepS68).

It should be noted that, before summing up the feature value, theapplication 241 preliminarily screens the third threshold value. Forexample, if the user does not focus an obvious recognition target whichresults in that the feature value generated from the matching betweeneach matching data 242 and captured frame is less than the thirdthreshold value (i.e. the feature value is not summed up). Under thecircumstance, given the application 241 captured a hundred frames, andmatching a hundred times, the summing feature value of each matchingdata 242 does not exceed the second threshold value. That means the userdoes not obtain a candidate result which is totally dissimilar with therecognition target 1.

After the step S68, the application 241 clears the content of thesimilar list 244 (step S70). That is clearing the summing feature valueswritten in the similar list S244. Additionally, when the candidateresult is displayed, the application 241 may selectively delete theresult list 243. If the recognition obtains several the optimal resultsor the candidate results, the application 241 sorts the feature valuesnumerically, and displays based on the sorting order on the displaymonitor 21 for user's searching reference.

In order to elaborate the recognition method, an embodiment is used inthe following. In the embodiment, the first threshold value is set as“20”, the second threshold value is set as “35”, and the third thresholdvalue is set as “3”. When the application 241 captures the first frame,and performs recognition on the first frame, the following first resultlist is generated according to the recognition result:

Matching Data Feature Value First Threshold Value A 2 20 B 12 20 C 15 20

First Result List

As the first result list described, the application 241 performsrecognition on the first frame and does not obtain an optimal result,and sums up the feature value of each matching data to the similar list244. Because the feature value of the matching data A is less than thethird threshold value, and is not summed up, and the content of thesimilar list 244 is listed as the following:

Matching Data Summing Feature Value Second Threshold Value A 0 35 B 1235 C 15 35

Similar List

As described in the similar list 244, because no summing feature valueof the matching data is higher than the second threshold value, theapplication 241 does not obtain a candidate result. Next, theapplication 241 obtains the second frame, and performs recognition onthe second frame, and generates the following second list according tothe recognition result:

Matching Data Feature Value First Threshold Value A 10 20 B 12 20 C 1720

Second Result List

As described in the second result list, the application 241 performsrecognition on the second frame and does not obtain optimal result, andsums up the feature value of each matching data to the similar list 244,and the content of the similar list 244 is listed as the following:

Matching Data Summing Feature Value Second Threshold Value A 10(0 + 10) 35 B 24(12 + 12) 35 C 32(15 + 17) 35

Similar List

As described in the similar list 244, after the summing up operation, nosumming feature value of the matching data is higher than the secondthreshold value. The application 241 still does not obtain a candidateresult. Next, the application 241 obtains a third frame, and performsthe recognition on the third frame, and generates the following thirdresult list according to the recognition result:

Matching Data Feature Value First Threshold Value A 5 20 B 13 20 C 15 20

Third Result List

As described in the third result list, the application 241 performsrecognition on the third frame and does not obtain optimal result, andsums up the feature value of each matching data to the similar list 244,and the content of the similar list 244 is listed as the following:

Matching Data Summing Feature Value Second Threshold Value A 15(0 + 10 +5)  35 B 37(12 + 12 + 13) 35 C 47(15 + 17 + 15) 35

Similar List

As described in the similar list 244, after the summing up operation,the summing feature values of the matching data B and matching data Care higher than the second threshold value. The application 241considers the matching data B and the matching data C as candidateresults, and displays in the display monitor 21. In the embodiment, thesumming feature value of the matching data C is higher than the summingfeature value of the matching data B, the application 21 may onlydisplay the matching data C, or display the matching data C and thematching data B by the numerical order of the summing feature value.

In the recognition method, after the recognition of each frame, theapplication 241 first determines if optimal result is generated, if nooptimal result, then the feature value is summed up to the similar list244. In other words, the application 241 generates the result list 243for each frame, and, the application 241 deletes the result list 243when displays an optimal result, a candidate result, or the featurevalues of the result list 243 are all zeros or less than the thirdthreshold value. Additionally, when perform recognition on the nextframe, the application 241 generates a new result list 243 for the nextframe.

Different from the result list 243, the similar list 244 is the resultof summing up the feature values, and the similar list 244 remains toexist and is not deleted. Additionally, after an optimal result isdisplayed, a candidate result is displayed, the mobile device 2 movesand the moving amount exceeding a predetermined threshold value, or thefeature values of the result list 243 are all zeros or less than thethird threshold value, the application 241 clears the content of thesimilar list 244, which means, clears the summing feature values writtenin the similar list 244.

With the recognition method of the present invention, when therecognition of a frame 3 succeeds, the mobile device 2 provides anoptimal result for user's searching reference. When the recognition of aframe 3 fails, the mobile device 2 continues to perform recognition onthe second frame, the third frame to the nth frame, and uses themechanism of the summing feature value for obtaining a candidate resultin the end for user's searching reference. In other words, given thatthe capturing angles and lighting of single one frame 3 are not idealfor performing recognition, the recognition based on the method of thepresent invention does not fail if any matching data 242 identical withthe recognition target.

As the skilled person will appreciate, various changes and modificationscan be made to the described embodiments. It is intended to include allsuch variations, modifications and equivalents which fall within thescope of the invention, as defined in the accompanying claims.

What is claimed is:
 1. A cumulative image recognition method for amobile device, wherein a plurality of the matching data is saved in themobile device, the cumulative image recognition method comprising: a)capturing frames of a recognition target via an image capturing unit ofa mobile device; b) performing matching analysis on the frame and theplurality of the matching data; c) respectively obtaining feature valuesof each matching data according to the analysis result; d) determiningif the feature value of the matching data is higher than a firstthreshold value; e) the matching data being an optimal result if thematching data has the feature value higher than the first thresholdvalue, and displaying the optimal result on a display monitor of themobile device; f) if the feature value of the matching data is nothigher than the first threshold value, writing the feature value of eachmatching data in an similar list for summing up the summing featurevalue of each matching data written in the similar list; g) determiningif the summing feature value of the matching data is higher than asecond threshold value; h) if the summing feature value of the matchingdata is not higher than the second threshold value, re-executing step ato step g; and i) the matching data being a candidate result if thematching data has the summing feature value higher than the secondthreshold value, and displaying the candidate result on the displaymonitor.
 2. The cumulative image recognition method of claim 1, whereinthe mobile device executes an image analysis via an application saved inthe mobile device, for respectively acquiring one or several featuringpoints identical between each matching data and the frame, the featurevalue is the quantity of the one or several featuring points.
 3. Thecumulative image recognition method of claim 2, wherein the secondthreshold value is higher than the first threshold value.
 4. Thecumulative image recognition method of claim 2, wherein the step acomprising following steps: a1) initiating the application and theapplication controlling the image capturing unit to enter into a cameracapturing model; a2) the image capturing unit scanning the externalimage for determining if the mobile device moves via scanning theexternal image the image capturing unit; a3) if the mobile device doesnot move, the image capturing unit capturing the frame.
 5. Thecumulative image recognition method of claim 4, wherein the methodfurther comprises a step a4: following step a3, if the mobile devicemoves, and the move value exceeds a predetermined threshold value,clearing the summing feature values written in the similar list.
 6. Thecumulative image recognition method of claim 2, wherein furthercomprises a step j: after step e and step h, clearing the summingfeature values written in the similar list.
 7. The cumulative imagerecognition method of claim 2, wherein further comprising followingsteps: k) following step c, establishing a result list according to thefeature value of each matching data; l) determining if the featurevalues in the result list are all zero or all less than a thirdthreshold value; m) if the feature values in the result list are allzero or all less than a third threshold value, clearing the summingfeature values written in the similar list; and n) if the feature valuesin the result list are not all zero or all less than a third thresholdvalue, continuing to execute step d.
 8. The cumulative image recognitionmethod of claim 7, wherein the step m comprising following steps: m1) ifthe feature values in the result list are all zero or all less than athird threshold value, re-executing step a, b, c, k and l; m2) followingstep m1, if the feature values in the result list still are all zero orall less than a third threshold value, clearing the summing featurevalues written in the similar list.
 9. The cumulative image recognitionmethod of claim 7, wherein further comprises a step o: after step e, h,m, deleting the result list.
 10. The cumulative image recognition methodof claim 2, wherein in step f, the mobile device only sums up thefeature value high than a third threshold value with the similar list,wherein the third threshold value is less than the first threshold valueand the second threshold value.
 11. An application with executableprogramming codes for a mobile device written, when the mobile deviceexecuting the application, execute the steps mentioned in the claim 1.